All Data clusters (Deep learning, DEL (Deep Embedding Clustering layer))
DEC_Embedding = read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/DEC_Embedding.csv')
head(DEC_Embedding)

resultft_DEL_all <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/resultft_DEL_all.csv')
# replacing the empty space "" values with no as done in the main analysis file
resultft_DEL_all$farmlive[resultft_DEL_all$farmlive == ""] <- NA
resultft_DEL_all <- resultft_DEL_all %>% replace_na (list(farmlive = 'no'))
#tsne_converted_food$cl_DEL <- factor(resultft_DEL_all$cluster)
#ggplot(tsne_converted_food, aes(x=X, y=Y, color=cl_DEL)) + geom_point()
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
dist_plot_clust <-function(original_data, selected_variable){
selected_variable <- enquo(selected_variable)
ggplot(original_data, aes(UQ(selected_variable))) + geom_density(aes(fill = factor(cluster)), alpha=0.8) +
labs(title = "Density plot",
subtitle="sIgE_f1 of persons Grouped by Clusters",
caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
x="sIgE_f1",
fill="# Clusters")
}
dist_plot_clust(original_data = resultft_DEL_all, selected_variable = age)

Density plot shoiwing the age distribution for each cluster
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
age_g <- ggplot(resultft_DEL_all, aes(sIgE_f3))
age_p <- age_g + geom_density(aes(fill=factor(cluster)), alpha=0.8) +
labs(title="Density plot",
subtitle="sIgE_f1 of persons Grouped by Clusters",
caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
x="sIgE_f1",
fill="# Clusters")
ggplotly(age_p)
g <- ggplot(resultft_DEL_all, aes(bmi2)) + scale_fill_brewer(palette = "Spectral")
s <- g + geom_histogram(aes(fill=factor(cluster)),
bins=5,
col="black",
size=.1) + # change number of bins
labs(title="Histogram with Fixed Bins",
subtitle="Age across different clusters",
x="Age",
fill="# Clusters")
ggplotly(s)
table_uft_DEL_all <- tableby(cluster ~ ., data = as.list(resultft_DEL_all))
summary(table_uft_DEL_all, title = "Charachtaristcs of Clusters")
Table: Charachtaristcs of Clusters
| sIgE_f1 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.030 (0.122) |
0.068 (0.493) |
0.000 (0.000) |
1.076 (7.174) |
0.082 (1.747) |
|
| Range |
0.000 - 1.060 |
0.000 - 8.179 |
0.000 - 0.000 |
0.000 - 73.692 |
0.000 - 73.692 |
|
| sIgE_f2 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.038 (0.138) |
0.082 (0.364) |
0.000 (0.000) |
0.610 (2.148) |
0.060 (0.558) |
|
| Range |
0.000 - 1.091 |
0.000 - 3.807 |
0.000 - 0.000 |
0.000 - 13.623 |
0.000 - 13.623 |
|
| sIgE_f3 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.004 (0.013) |
0.008 (0.022) |
0.000 (0.000) |
0.113 (0.261) |
0.009 (0.069) |
|
| Range |
0.000 - 0.080 |
0.000 - 0.137 |
0.000 - 0.000 |
0.000 - 1.332 |
0.000 - 1.332 |
|
| sIgE_f4 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.018 (0.095) |
0.093 (0.379) |
0.000 (0.000) |
0.886 (1.944) |
0.073 (0.534) |
|
| Range |
0.000 - 1.280 |
0.000 - 4.347 |
0.000 - 0.000 |
0.000 - 12.512 |
0.000 - 12.512 |
|
| sIgE_f13 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.030 (0.091) |
0.220 (0.759) |
0.000 (0.001) |
4.809 (19.740) |
0.324 (4.861) |
|
| Range |
0.000 - 0.927 |
0.000 - 11.866 |
0.000 - 0.020 |
0.000 - 149.746 |
0.000 - 149.746 |
|
| sIgE_f14 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.004 (0.019) |
0.033 (0.125) |
0.000 (0.000) |
0.684 (1.847) |
0.046 (0.472) |
|
| Range |
0.000 - 0.310 |
0.000 - 1.086 |
0.000 - 0.000 |
0.000 - 12.386 |
0.000 - 12.386 |
|
| sIgE_f17 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.312 (0.873) |
2.484 (3.286) |
0.000 (0.013) |
22.025 (21.996) |
1.794 (7.461) |
|
| Range |
0.000 - 11.197 |
0.000 - 13.628 |
0.000 - 0.398 |
0.000 - 111.259 |
0.000 - 111.259 |
|
| sIgE_f18 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.017 (0.168) |
0.017 (0.039) |
0.001 (0.025) |
0.579 (4.510) |
0.042 (1.091) |
|
| Range |
0.000 - 2.901 |
0.000 - 0.384 |
0.000 - 0.686 |
0.000 - 46.879 |
0.000 - 46.879 |
|
| sIgE_f20 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.011 (0.027) |
0.094 (0.142) |
0.000 (0.001) |
1.106 (1.703) |
0.083 (0.485) |
|
| Range |
0.000 - 0.193 |
0.000 - 0.915 |
0.000 - 0.015 |
0.000 - 9.959 |
0.000 - 9.959 |
|
| sIgE_f36 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.011 (0.025) |
0.067 (0.129) |
0.000 (0.002) |
0.412 (1.090) |
0.039 (0.283) |
|
| Range |
0.000 - 0.225 |
0.000 - 1.051 |
0.000 - 0.045 |
0.000 - 7.754 |
0.000 - 7.754 |
|
| gender2 |
|
|
|
|
|
< 0.001 |
| females |
329 (62.5%) |
185 (56.4%) |
453 (49.8%) |
55 (50.9%) |
1022 (54.6%) |
|
| males |
197 (37.5%) |
143 (43.6%) |
457 (50.2%) |
53 (49.1%) |
850 (45.4%) |
|
| age |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
47.196 (15.214) |
48.255 (15.130) |
51.199 (15.617) |
41.367 (14.267) |
48.991 (15.551) |
|
| Range |
18.146 - 76.877 |
19.058 - 78.075 |
18.875 - 77.746 |
19.415 - 77.130 |
18.146 - 78.075 |
|
| bmi2 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
27.698 (3.266) |
30.328 (6.497) |
24.411 (2.776) |
26.762 (4.942) |
26.507 (4.540) |
|
| Range |
18.904 - 34.816 |
18.290 - 50.058 |
16.975 - 31.556 |
17.915 - 38.955 |
16.975 - 50.058 |
|
| farmlive |
|
|
|
|
|
|
| |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
|
| no |
460 (87.5%) |
292 (89.0%) |
789 (86.7%) |
107 (99.1%) |
1648 (88.0%) |
|
| yes |
66 (12.5%) |
36 (11.0%) |
121 (13.3%) |
1 (0.9%) |
224 (12.0%) |
|
| family_allergy_hist |
|
|
|
|
|
< 0.001 |
| no |
229 (43.5%) |
132 (40.2%) |
633 (69.6%) |
28 (25.9%) |
1022 (54.6%) |
|
| yes |
297 (56.5%) |
196 (59.8%) |
277 (30.4%) |
80 (74.1%) |
850 (45.4%) |
|
# adding the id variable
#result_food_uft_DEL_k$ID <- food_data_id$ID
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
Charachtiristic Analysis
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
catdes(resultft_DEL_all, 16)
Random Data clusters with DEL (Deep Embedding Clustering layer)
#result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_food_uft_DEL_k.csv")
result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_uft_DEL_k.csv")
result_rand_uft_DEL_k$farmlive[result_rand_uft_DEL_k$farmlive == ""] <- NA
result_rand_uft_DEL_k <- result_rand_uft_DEL_k %>% replace_na (list(farmlive = 'no'))
table_rand_uft_DEL_k <- tableby(cluster ~ ., data = as.list(result_rand_uft_DEL_k))
summary(table_rand_uft_DEL_k, title = "Charachtaristcs of Clusters")
Table: Charachtaristcs of Clusters
| sIgE_f1 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.001 (0.009) |
0.000 (0.000) |
0.067 (0.291) |
0.044 (0.149) |
0.016 (0.117) |
|
| Range |
0.000 - 0.092 |
0.000 - 0.000 |
0.000 - 2.845 |
0.000 - 1.032 |
0.000 - 2.845 |
|
| sIgE_f2 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.009 (0.066) |
0.000 (0.000) |
0.186 (0.829) |
0.042 (0.148) |
0.030 (0.277) |
|
| Range |
0.000 - 0.640 |
0.000 - 0.000 |
0.000 - 6.849 |
0.000 - 1.091 |
0.000 - 6.849 |
|
| sIgE_f3 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.001 (0.006) |
0.000 (0.000) |
0.016 (0.042) |
0.006 (0.017) |
0.003 (0.016) |
|
| Range |
0.000 - 0.045 |
0.000 - 0.000 |
0.000 - 0.213 |
0.000 - 0.080 |
0.000 - 0.213 |
|
| sIgE_f4 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.005 (0.076) |
0.000 (0.000) |
0.238 (0.945) |
0.048 (0.222) |
0.035 (0.324) |
|
| Range |
0.000 - 1.280 |
0.000 - 0.000 |
0.000 - 8.417 |
0.000 - 2.543 |
0.000 - 8.417 |
|
| sIgE_f13 |
|
|
|
|
|
0.002 |
| Mean (SD) |
0.017 (0.077) |
0.002 (0.030) |
1.591 (12.755) |
0.107 (0.312) |
0.187 (4.042) |
|
| Range |
0.000 - 0.800 |
0.000 - 0.642 |
0.000 - 133.659 |
0.000 - 1.957 |
0.000 - 133.659 |
|
| sIgE_f14 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.003) |
0.000 (0.000) |
0.203 (0.946) |
0.020 (0.092) |
0.025 (0.306) |
|
| Range |
0.000 - 0.040 |
0.000 - 0.000 |
0.000 - 8.505 |
0.000 - 0.833 |
0.000 - 8.505 |
|
| sIgE_f17 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.267 (0.781) |
0.007 (0.066) |
9.076 (15.899) |
0.921 (2.133) |
1.174 (5.760) |
|
| Range |
0.000 - 4.561 |
0.000 - 1.014 |
0.000 - 76.467 |
0.000 - 11.197 |
0.000 - 76.467 |
|
| sIgE_f18 |
|
|
|
|
|
0.012 |
| Mean (SD) |
0.005 (0.042) |
0.001 (0.015) |
0.477 (4.469) |
0.026 (0.219) |
0.055 (1.416) |
|
| Range |
0.000 - 0.686 |
0.000 - 0.322 |
0.000 - 46.879 |
0.000 - 2.901 |
0.000 - 46.879 |
|
| sIgE_f20 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.010 (0.034) |
0.000 (0.004) |
0.399 (1.057) |
0.043 (0.117) |
0.052 (0.357) |
|
| Range |
0.000 - 0.272 |
0.000 - 0.057 |
0.000 - 9.959 |
0.000 - 0.915 |
0.000 - 9.959 |
|
| sIgE_f36 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.009 (0.026) |
0.001 (0.005) |
0.163 (0.763) |
0.036 (0.112) |
0.027 (0.250) |
|
| Range |
0.000 - 0.235 |
0.000 - 0.067 |
0.000 - 7.754 |
0.000 - 1.051 |
0.000 - 7.754 |
|
| gender2 |
|
|
|
|
|
0.229 |
| females |
150 (52.6%) |
263 (55.7%) |
58 (52.7%) |
112 (47.5%) |
583 (52.9%) |
|
| males |
135 (47.4%) |
209 (44.3%) |
52 (47.3%) |
124 (52.5%) |
520 (47.1%) |
|
| age |
|
|
|
|
|
0.001 |
| Mean (SD) |
49.396 (14.935) |
51.639 (15.931) |
45.611 (14.833) |
51.412 (14.636) |
50.410 (15.390) |
|
| Range |
18.146 - 77.746 |
18.875 - 77.259 |
20.867 - 77.130 |
18.379 - 76.628 |
18.146 - 77.746 |
|
| bmi2 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
26.126 (2.338) |
23.553 (2.460) |
31.668 (6.216) |
28.599 (3.067) |
26.107 (4.127) |
|
| Range |
19.223 - 31.644 |
16.975 - 29.835 |
18.939 - 44.816 |
19.818 - 36.523 |
16.975 - 44.816 |
|
| farmlive |
|
|
|
|
|
|
| |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
|
| no |
252 (88.4%) |
382 (80.9%) |
107 (97.3%) |
219 (92.8%) |
960 (87.0%) |
|
| yes |
33 (11.6%) |
90 (19.1%) |
3 (2.7%) |
17 (7.2%) |
143 (13.0%) |
|
| family_allergy_hist |
|
|
|
|
|
< 0.001 |
| no |
164 (57.5%) |
361 (76.5%) |
38 (34.5%) |
132 (55.9%) |
695 (63.0%) |
|
| yes |
121 (42.5%) |
111 (23.5%) |
72 (65.5%) |
104 (44.1%) |
408 (37.0%) |
|
# adding the id variable
#result_food_uft_DEL_k$ID <- food_data_id$ID
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
Charachtiristic Analysis
result_rand_uft_DEL_k$cluster <- as.factor(result_rand_uft_DEL_k$cluster)
#result_food_uft_DEL_k <- result_food_uft_DEL_k[-c(1,2,20)]
catdes(result_rand_uft_DEL_k, 16)
With asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)
result_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_as_rh_uft_DEL.csv')
result_as_rh_uft_DEL$farmlive[result_as_rh_uft_DEL$farmlive == ""] <- NA
result_as_rh_uft_DEL <- result_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_as_rh_uft_DEL))
summary(table_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")
Table: Charachtaristcs of Clusters
| sIgE_f1 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.008 (0.070) |
0.527 (1.012) |
0.088 (0.546) |
1.446 (8.885) |
0.136 (2.300) |
|
| Range |
0.000 - 1.060 |
0.010 - 4.695 |
0.000 - 8.179 |
0.000 - 73.692 |
0.000 - 73.692 |
|
| sIgE_f2 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.004 (0.035) |
2.043 (2.909) |
0.056 (0.149) |
0.293 (1.632) |
0.094 (0.728) |
|
| Range |
0.000 - 0.551 |
0.034 - 13.006 |
0.000 - 0.850 |
0.000 - 13.623 |
0.000 - 13.623 |
|
| sIgE_f3 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.001 (0.006) |
0.243 (0.361) |
0.011 (0.024) |
0.067 (0.194) |
0.015 (0.090) |
|
| Range |
0.000 - 0.080 |
0.015 - 1.244 |
0.000 - 0.137 |
0.000 - 1.332 |
0.000 - 1.332 |
|
| sIgE_f4 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.002 (0.014) |
0.538 (1.328) |
0.107 (0.316) |
1.063 (2.256) |
0.112 (0.687) |
|
| Range |
0.000 - 0.224 |
0.030 - 7.445 |
0.000 - 2.275 |
0.000 - 12.512 |
0.000 - 12.512 |
|
| sIgE_f13 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.022 (0.094) |
6.986 (25.028) |
0.287 (0.859) |
1.913 (4.129) |
0.411 (4.497) |
|
| Range |
0.000 - 0.927 |
0.017 - 133.659 |
0.000 - 11.866 |
0.000 - 23.815 |
0.000 - 133.659 |
|
| sIgE_f14 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.001 (0.007) |
0.430 (1.026) |
0.052 (0.182) |
0.704 (2.127) |
0.071 (0.601) |
|
| Range |
0.000 - 0.160 |
0.000 - 4.468 |
0.000 - 1.475 |
0.000 - 12.386 |
0.000 - 12.386 |
|
| sIgE_f17 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.150 (0.435) |
1.714 (3.833) |
4.567 (5.110) |
25.396 (26.534) |
2.937 (9.526) |
|
| Range |
0.000 - 2.715 |
0.000 - 17.146 |
0.000 - 23.778 |
0.000 - 111.259 |
0.000 - 111.259 |
|
| sIgE_f18 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.006 (0.069) |
0.068 (0.137) |
0.042 (0.217) |
0.818 (5.599) |
0.069 (1.436) |
|
| Range |
0.000 - 1.678 |
0.000 - 0.585 |
0.000 - 2.901 |
0.000 - 46.879 |
0.000 - 46.879 |
|
| sIgE_f20 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.008 (0.029) |
0.392 (0.776) |
0.161 (0.277) |
1.179 (2.058) |
0.133 (0.625) |
|
| Range |
0.000 - 0.343 |
0.000 - 3.849 |
0.000 - 1.920 |
0.000 - 9.959 |
0.000 - 9.959 |
|
| sIgE_f36 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.009 (0.028) |
0.100 (0.180) |
0.082 (0.157) |
0.487 (1.335) |
0.061 (0.368) |
|
| Range |
0.000 - 0.277 |
0.000 - 0.845 |
0.000 - 1.051 |
0.000 - 7.754 |
0.000 - 7.754 |
|
| gender2 |
|
|
|
|
|
0.354 |
| females |
408 (57.6%) |
16 (51.6%) |
149 (55.4%) |
33 (47.1%) |
606 (56.2%) |
|
| males |
300 (42.4%) |
15 (48.4%) |
120 (44.6%) |
37 (52.9%) |
472 (43.8%) |
|
| age |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
49.324 (15.421) |
48.398 (19.396) |
44.784 (14.798) |
40.863 (12.795) |
47.615 (15.443) |
|
| Range |
19.266 - 76.656 |
19.415 - 77.130 |
19.058 - 76.190 |
20.741 - 78.075 |
19.058 - 78.075 |
|
| bmi2 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
25.960 (3.499) |
28.986 (5.065) |
27.843 (5.614) |
30.970 (8.239) |
26.842 (4.790) |
|
| Range |
17.404 - 34.484 |
19.044 - 38.514 |
17.915 - 40.083 |
20.381 - 50.058 |
17.404 - 50.058 |
|
| farmlive |
|
|
|
|
|
|
| |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
|
| no |
637 (90.0%) |
29 (93.5%) |
238 (88.5%) |
64 (91.4%) |
968 (89.8%) |
|
| yes |
71 (10.0%) |
2 (6.5%) |
31 (11.5%) |
6 (8.6%) |
110 (10.2%) |
|
| family_allergy_hist |
|
|
|
|
|
< 0.001 |
| no |
347 (49.0%) |
8 (25.8%) |
98 (36.4%) |
22 (31.4%) |
475 (44.1%) |
|
| yes |
361 (51.0%) |
23 (74.2%) |
171 (63.6%) |
48 (68.6%) |
603 (55.9%) |
|
# adding the id variable
#result_food_uft_DEL_k$ID <- food_data_id$ID
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
Charachtiristic Analysis
result_as_rh_uft_DEL$cluster <- as.factor(result_as_rh_uft_DEL$cluster)
catdes(result_as_rh_uft_DEL, 16)
Without asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)
result_no_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_no_as_rh_uft_DEL.csv')
result_no_as_rh_uft_DEL$farmlive[result_no_as_rh_uft_DEL$farmlive == ""] <- NA
result_no_as_rh_uft_DEL <- result_no_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_no_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_no_as_rh_uft_DEL))
summary(table_no_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")
Table: Charachtaristcs of Clusters
| sIgE_f1 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.053 (0.148) |
0.017 (0.045) |
0.005 (0.019) |
0.000 (0.000) |
0.010 (0.064) |
|
| Range |
0.000 - 0.918 |
0.000 - 0.149 |
0.000 - 0.098 |
0.000 - 0.000 |
0.000 - 0.918 |
|
| sIgE_f2 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.059 (0.166) |
0.055 (0.156) |
0.046 (0.290) |
0.000 (0.000) |
0.014 (0.106) |
|
| Range |
0.000 - 1.091 |
0.000 - 0.559 |
0.000 - 2.143 |
0.000 - 0.000 |
0.000 - 2.143 |
|
| sIgE_f3 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.008 (0.018) |
0.017 (0.043) |
0.004 (0.014) |
0.000 (0.000) |
0.002 (0.011) |
|
| Range |
0.000 - 0.067 |
0.000 - 0.134 |
0.000 - 0.065 |
0.000 - 0.000 |
0.000 - 0.134 |
|
| sIgE_f4 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.043 (0.148) |
0.254 (0.653) |
0.097 (0.462) |
0.000 (0.000) |
0.019 (0.164) |
|
| Range |
0.000 - 1.129 |
0.000 - 2.031 |
0.000 - 2.543 |
0.000 - 0.000 |
0.000 - 2.543 |
|
| sIgE_f13 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.046 (0.165) |
10.907 (39.964) |
0.099 (0.280) |
0.000 (0.000) |
0.207 (5.315) |
|
| Range |
0.000 - 1.596 |
0.000 - 149.746 |
0.000 - 1.486 |
0.000 - 0.000 |
0.000 - 149.746 |
|
| sIgE_f14 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.011 (0.047) |
0.456 (1.336) |
0.028 (0.122) |
0.000 (0.000) |
0.012 (0.185) |
|
| Range |
0.000 - 0.366 |
0.000 - 4.886 |
0.000 - 0.728 |
0.000 - 0.000 |
0.000 - 4.886 |
|
| sIgE_f17 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.408 (1.130) |
4.284 (10.816) |
1.412 (4.447) |
0.000 (0.000) |
0.242 (1.977) |
|
| Range |
0.000 - 7.065 |
0.000 - 36.193 |
0.000 - 19.736 |
0.000 - 0.000 |
0.000 - 36.193 |
|
| sIgE_f18 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.007 (0.032) |
0.096 (0.248) |
0.016 (0.050) |
0.000 (0.000) |
0.004 (0.039) |
|
| Range |
0.000 - 0.322 |
0.000 - 0.868 |
0.000 - 0.261 |
0.000 - 0.000 |
0.000 - 0.868 |
|
| sIgE_f20 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.025 (0.073) |
0.366 (0.762) |
0.067 (0.171) |
0.000 (0.000) |
0.015 (0.122) |
|
| Range |
0.000 - 0.520 |
0.000 - 2.149 |
0.000 - 0.849 |
0.000 - 0.000 |
0.000 - 2.149 |
|
| sIgE_f36 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.021 (0.060) |
0.140 (0.315) |
0.038 (0.121) |
0.000 (0.000) |
0.009 (0.061) |
|
| Range |
0.000 - 0.366 |
0.000 - 1.081 |
0.000 - 0.734 |
0.000 - 0.000 |
0.000 - 1.081 |
|
| gender2 |
|
|
|
|
|
0.039 |
| females |
57 (42.5%) |
6 (42.9%) |
26 (47.3%) |
327 (55.3%) |
416 (52.4%) |
|
| males |
77 (57.5%) |
8 (57.1%) |
29 (52.7%) |
264 (44.7%) |
378 (47.6%) |
|
| age |
|
|
|
|
|
0.472 |
| Mean (SD) |
50.340 (15.466) |
50.315 (14.955) |
47.934 (14.331) |
51.261 (15.644) |
50.858 (15.512) |
|
| Range |
18.379 - 76.877 |
22.091 - 74.242 |
19.428 - 74.099 |
18.146 - 77.746 |
18.146 - 77.746 |
|
| bmi2 |
|
|
|
|
|
< 0.001 |
| Mean (SD) |
28.253 (3.789) |
37.507 (7.834) |
32.104 (3.784) |
24.719 (2.772) |
26.053 (4.136) |
|
| Range |
18.904 - 37.109 |
22.097 - 46.094 |
23.356 - 40.164 |
16.975 - 32.076 |
16.975 - 46.094 |
|
| farmlive |
|
|
|
|
|
|
| |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
|
| no |
112 (83.6%) |
13 (92.9%) |
48 (87.3%) |
507 (85.8%) |
680 (85.6%) |
|
| yes |
22 (16.4%) |
1 (7.1%) |
7 (12.7%) |
84 (14.2%) |
114 (14.4%) |
|
| family_allergy_hist |
|
|
|
|
|
0.978 |
| no |
93 (69.4%) |
10 (71.4%) |
39 (70.9%) |
405 (68.5%) |
547 (68.9%) |
|
| yes |
41 (30.6%) |
4 (28.6%) |
16 (29.1%) |
186 (31.5%) |
247 (31.1%) |
|
# adding the id variable
#result_food_uft_DEL_k$ID <- food_data_id$ID
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
Charachtiristic Analysis
result_no_as_rh_uft_DEL$cluster <- as.factor(result_no_as_rh_uft_DEL$cluster)
catdes(result_no_as_rh_uft_DEL, 16)
adding the id varaible
resultft_DEL_all$ID <- food_data_id$ID
result_rand_uft_DEL_k$ID <- rand_food_data_id$ID
result_as_rh_uft_DEL$ID <- as_ri_food_id$ID
result_no_as_rh_uft_DEL$ID <- no_as_ri_food_id$ID
write.csv(resultft_DEL_all,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/resultft_DEL_all_id.csv')
write.csv(result_rand_uft_DEL_k,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_rand_uft_DEL_k_id.csv')
write.csv(result_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_as_rh_uft_DEL_id.csv')
write.csv(result_no_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_no_as_rh_uft_DEL_id.csv')
---
title: "Results food data clustering DEL"
output:
  html_notebook: default
  pdf_document: default
---

```{r loadlib, include=FALSE}
library(FactoMineR)
library(factoextra)
library(arsenal)
library(Rtsne)
library(plotly)
library(tidyverse)
```

# All Data clusters (Deep learning, DEL (Deep Embedding Clustering layer))

```{r}
DEC_Embedding = read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/DEC_Embedding.csv')
head(DEC_Embedding)
```

```{r}
set.seed(10)
#tsne_converted_food_DEL <- Rtsne(X = EDL_Embedding ,perplexity= 200, is_distance = FALSE, check_duplicates = FALSE)
tsne_converted_food_DEC <- Rtsne(X = DEC_Embedding ,perplexity= 150, is_distance = FALSE, check_duplicates = FALSE)

tsne_converted_food_DEC <- tsne_converted_food_DEC$Y %>%
  data.frame() %>%
  setNames(c("X", "Y"))

tsne_converted_food_DEC$cl <- factor(resultft_DEL_all$cluster)
ggplot(tsne_converted_food_DEC, aes(x=X, y=Y, color=cl)) + geom_point()

#ggplot(aes(x = X, y = Y), data = tsne_converted_food_DEC)  + geom_point()
```

```{r}
tsne_converted_food_DEC_3d <- Rtsne(X = DEC_Embedding ,perplexity= 150, dims = 3, is_distance = FALSE, check_duplicates = FALSE)

tsne_converted_food_DEC_3d <- tsne_converted_food_DEC_3d$Y %>%
  data.frame() %>%
  setNames(c("X", "Y", "Z"))

tsne_converted_food_DEC_3d$cl <- factor(resultft_DEL_all$cluster)

p <- plot_ly(tsne_converted_food_DEC_3d, x = ~X, y = ~Y, z = ~Z, color = ~cl, colors = c('#BF382A', '#0C4B8E')) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2'),
                     zaxis = list(title = 'Dim3')))
p
```

```{r}
resultft_DEL_all <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/resultft_DEL_all.csv')
# replacing the empty space "" values with no as done in the main analysis file
resultft_DEL_all$farmlive[resultft_DEL_all$farmlive == ""] <- NA
resultft_DEL_all <-  resultft_DEL_all %>% replace_na (list(farmlive = 'no'))
#tsne_converted_food$cl_DEL <- factor(resultft_DEL_all$cluster)
#ggplot(tsne_converted_food, aes(x=X, y=Y, color=cl_DEL)) + geom_point()
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
```

```{r}
dist_plot_clust <-function(original_data, selected_variable){
  selected_variable <- enquo(selected_variable)
  ggplot(original_data, aes(UQ(selected_variable))) + geom_density(aes(fill = factor(cluster)), alpha=0.8) +
    labs(title = "Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")
} 
```

```{r}
dist_plot_clust(original_data = resultft_DEL_all, selected_variable = age)
```


### Density plot shoiwing the age distribution for each cluster
```{r}
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)

age_g <- ggplot(resultft_DEL_all, aes(sIgE_f3))
age_p <- age_g + geom_density(aes(fill=factor(cluster)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")

ggplotly(age_p)
```


```{r}
g <- ggplot(resultft_DEL_all, aes(bmi2)) + scale_fill_brewer(palette = "Spectral")
s <- g + geom_histogram(aes(fill=factor(cluster)), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Age across different clusters",
       x="Age",
         fill="# Clusters") 

ggplotly(s)
```

```{r}
table_uft_DEL_all <- tableby(cluster ~ ., data = as.list(resultft_DEL_all))
summary(table_uft_DEL_all, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```



## Charachtiristic Analysis
```{r, warning=FALSE}
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
catdes(resultft_DEL_all, 16)
```



# Random Data clusters with DEL (Deep Embedding Clustering layer)

```{r}
#result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_food_uft_DEL_k.csv")
result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_uft_DEL_k.csv")
result_rand_uft_DEL_k$farmlive[result_rand_uft_DEL_k$farmlive == ""] <- NA
result_rand_uft_DEL_k <-  result_rand_uft_DEL_k %>% replace_na (list(farmlive = 'no'))
table_rand_uft_DEL_k <- tableby(cluster ~ ., data = as.list(result_rand_uft_DEL_k))
summary(table_rand_uft_DEL_k, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```

## Charachtiristic Analysis
```{r, warning=FALSE}
result_rand_uft_DEL_k$cluster <- as.factor(result_rand_uft_DEL_k$cluster)
#result_food_uft_DEL_k <- result_food_uft_DEL_k[-c(1,2,20)]
catdes(result_rand_uft_DEL_k, 16)
```

# With asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)

```{r}
result_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_as_rh_uft_DEL.csv')
result_as_rh_uft_DEL$farmlive[result_as_rh_uft_DEL$farmlive == ""] <- NA
result_as_rh_uft_DEL <-  result_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_as_rh_uft_DEL))
summary(table_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```

## Charachtiristic Analysis
```{r, warning=FALSE}
result_as_rh_uft_DEL$cluster <- as.factor(result_as_rh_uft_DEL$cluster)
catdes(result_as_rh_uft_DEL, 16)
```


# Without asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)

```{r}
result_no_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_no_as_rh_uft_DEL.csv')
result_no_as_rh_uft_DEL$farmlive[result_no_as_rh_uft_DEL$farmlive == ""] <- NA
result_no_as_rh_uft_DEL <-  result_no_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_no_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_no_as_rh_uft_DEL))
summary(table_no_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```

## Charachtiristic Analysis
```{r, warning=FALSE}
result_no_as_rh_uft_DEL$cluster <- as.factor(result_no_as_rh_uft_DEL$cluster)
catdes(result_no_as_rh_uft_DEL, 16)
```




# adding the id varaible
```{r}
resultft_DEL_all$ID <- food_data_id$ID
result_rand_uft_DEL_k$ID <- rand_food_data_id$ID
result_as_rh_uft_DEL$ID <- as_ri_food_id$ID
result_no_as_rh_uft_DEL$ID <- no_as_ri_food_id$ID
write.csv(resultft_DEL_all,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/resultft_DEL_all_id.csv')
write.csv(result_rand_uft_DEL_k,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_rand_uft_DEL_k_id.csv')
write.csv(result_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_as_rh_uft_DEL_id.csv')
write.csv(result_no_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_no_as_rh_uft_DEL_id.csv')
```



















